There are many existing models to incorporate graph structure in the forecasting task (e.g., [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}). Each of the models in this study has its own way of forecasting the time series given the input timeseries window and the adjacency matrix constructed by the graph learning module. For instance, MTGNN interchanges temporal convolution layers with graph convolution layers, and GTS uses a Diffusion-Convolutional Recurrent Neural Network (DCRNN) [1]}, where the hidden states of each node are diffused via graph convolutions at each timestep. Again, the core idea is that the adjacency matrix used in the graph-based forecasting is itself constructed in a differentiable way and can thus be adjusted by the model to improve forecasting results.
